AI in Media & Entertainment: starting the journey to value

The global media & entertainment industry has been lagging most other sectors in its adoption of artificial intelligence. Our research shows that M&E companies are set to close the gap over the next three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.

Special Report

AI in Media & Entertainment: starting the journey to value

Behind the curve on AI…

Rising adoption of artificial intelligence (AI) has been a key feature of the business landscape for the past few years. Now the COVID-19 crisis has strengthened the case for AI still further, by underlining the urgent need for fast, intelligent decision-making. But what’s the current state of play on AI adoption? And how is that set to change in the next three years?

To find out, we’ve teamed up with ESI ThoughtLab to conduct a global study of 1,200 organisations including 96 media & entertainment (M&E) companies. By drilling down into the findings, we’ve gained unprecedented insights into M&E companies’ current strategies and future aspirations for AI, and into what they need to do to turn those aspirations into reality.

What did our research tell us? The top line is that the industry has a long way to go to realise the full benefits of AI. Currently, M&E companies are in the early stages of AI adoption and maturity, well behind industries like automotive, banking, technology and healthcare. Just 1% of M&E businesses qualify as “AI leaders” against 15% across all industries. Hardly surprising then that the proportion of M&E companies that rate AI as being of high importance to their future is relatively low compared to other sectors (see Figure 1).

Figure 1: M&E respondents’ current view of AI’s importance.

0 10 20 30 40 50 60 70 80

Investment

Media

Energy

Consumer/retail

Insurance

All industries

Manufacturing

Telecom

Life sciences

Healthcare

Technology

Banks

Automotive

64%

77%

74%

66%

66%

64%

63%

60%

59%

52%

42%

75%

75%

AI in Media & Entertainment: starting the journey to value

…but looking to catch up…That said, the fact remains that majority of the M&E companies we surveyed do believe that AI is of high importance. And while most of the industry is currently in the early stages of AI adoption such as business case development or piloting, our research suggests the picture will be dramatically different in three years’ time. The findings indicate a fourfold increase globally and a 30% increase in Europe in the number of M&E companies that consider themselves to be in the mature stages of AI adoption – namely well-advanced in using AI to transform their businesses.

…as the competitive pressures in M&E intensifyWhat lies behind this concerted move to embrace AI? Media organisations have historically, operated as business-to-business (B2B) rather than business-to-consumer (B2C) organisations. One consequence was that unless they were vertically integrated with a platform, they did not own customer data first-hand. Instead, data was aggregated and interpreted for them by third-parties.

As a result, without strong technology advocates or disruptive voices at senior levels, M&E companies did not invest significantly in AI. In any case, lacking direct control over their end users’ experience, they could not have acted easily on many of the insights gained from it.

And the industry’s high barriers to entry meant it was protected from competitive pressures, meaning M&E organisations were making enough money without AI and – until Netflix emerged – were shielded from disruption.

All of this is now changing, with M&E companies facing ever-intensifying competition and increasingly getting their hands on end-user data through direct-to-consumer offerings. And what do they need to maximise the value of that data? AI.

AI in Media & Entertainment: starting the journey to value

Data management, RPA and chatbots lead the way As M&E companies map out their route to AI maturity, they’re targeting investment at a select group of technology areas. Our research shows that data management, robotic process automation (RPA) and digital assistants/chatbots – essentially basic AI – are at the top of their technology agendas both today and in the near future, with most of their AI-related budget allocated to these areas.

Interestingly, most are not considering the use of more advanced AI technologies like neuro-linguistic programming (NLP) or deep learning either now or in the near future. Based on our global cross-industry survey, we know that this stance is typical of companies in the early stages of AI adoption. Organisations become much more likely to be embrace advanced AI technologies as their AI adoption expands and the returns from it start to increase.

Building the business case for AI… The M&E industry’s historically low levels of competitive pressure and lack of directly-owned data help to explain why its uptake of AI has been so low until now – and how tricky it has been to build a solid business case for investing in it. But the sector is now at an inflection point. Given the huge amounts of data to come, media organisations cannot afford to take their eye off the ball, and must stay focused on building AI capabilities to make the most of that data.

However, it takes time to reach maturity – a business can’t expect to become an expert in the application of AI overnight. To compete effectively in the future, it’s imperative to start the journey now. Imagine a world where your competitors are able to act in an instant and automated way at a consumer-by-consumer level, feeding each individual with the perfect blend of content and personally targeted and tailored advertising…while you are still working on large audience segments and trying to guess what each one wants to watch. Fail to start investing in AI now, and that’s the future you could be looking at: one to be avoided if at all possible.

AI in Media & Entertainment: starting the journey to value

…and joining the fast-track To build a stronger business case for AI, M&E organisations need to assess their current situation vis-à-vis competitors, draw up a roadmap, and develop an understanding of how to extract data from products and use it to drive business processes and strategic decisions. With the exception of the largest media conglomerates, building the necessary capabilities purely in-house will be too slow-paced, too costly and not sufficiently innovative. Also, it’s very difficult to recruit people with the required skills and have sufficient scale to give them support and an attractive career path. All of this points to partnerships and outsourcing as the optimal approach.

Case study:

helping a global TV and media leader use AI to boost premium advertising revenues

Our client wanted to offer advertisers on its VOD platform a better experience and higher engagement rates, in turn driving improved ROI and increased ad spend while avoiding under- or over-selling. To demonstrate how it could do this, Cognizant ran a proof-of-concept using machine learning to forecast ad impressions around certain program types and times.

As our input we used 24 months of existing raw viewing information. After extracting key event data – views, engagements and clicks – we created and applied models to identify patterns in viewing and ad engagement, by correlating the event data with historical data (such as day of the week) and contextual data (such as video asset lifetime).

After six weeks the client was able to predict ad impressions with almost 90% accuracy on a single drama series. After a phased scale-up across more genres and channels where we explored ad impression patterns on just over 50 comedy series, an overall average model accuracy of up to about 90% was still achievable. Together with our client we confirmed that data modernisation and AI can directly improve operational efficiency, monetisation and customer experience in TV advertising.

Outsourcing on the rise M&E companies fully understand the huge commitment of time and expense required to build and run AI capabilities internally – and are looking outside for help. Almost two-thirds of respondents already outsource at least three or four areas related to AI. And outsourcing is set to increase still further over the next three years, with areas like model value measurement and model scoping in the forefront (see Figure 2). Companies also see outsourcing and/or partnerships with technology companies as a way to increase AI capabilities and skills for their organisation.

Reasons to collaborateAs we highlighted above, today’s fiercely competitive recruitment market can make it prohibitive to build an AI competency in-house, meaning collaborating externally is often a better route. The options include working with professional and technical services providers – who in turn partner with leading global platforms and like Google and Microsoft, as well as with innovative niche AI specialists such as Hive and StoryFit.

The need for training in AI skills is a further reason to collaborate with a partner for the AI journey. Training is tricky to scale in any industry: take small hospitals, which often lack the capacity to carry the burden of training doctors, and don’t have sufficient volume of patients requiring each specialism. Many M&E companies are in a similar position. But larger professional services providers specialising in technology can deliver a ready and sustainable supply of talent that keeps pace with the rapidly-evolving technology landscape, while also sharing lessons learned from client work in other industries.

AI in Media & Entertainment: starting the journey to value

20%

32%33%

15%

BeginnerDeveloping plans and

building internal support for AI

LeaderWidely using AI

to generate many benets and transform

business

ImplementerStarting to pilot

AI and use a few simple

applications

AdvancerUsing AI in key

parts of the business and seeing gains

Figure 2: M&E respondents’ areas for outsourcing today and in three years’ time.

Case study:

How StoryFit used AI to zoom on female representation in movies

It feels like progress is being made and women are getting a bigger say in the movies – including more lead roles and more compelling characters. But is this true? AI startup StoryFit set out to find the real story, by using its technology to analyse female representation in the nominees for the 2019 Oscars. The project proved to be a powerful case study of AI’s relevance in M&E.

By applying its AI-driven software to scrutinise the roles and characters played by different genders in the Oscar nominees, and comparing the findings with its previous analysis of some 30,000 film scripts between 1930 and 2018, StoryFit generated unprecedented insights into women’s evolving status in the movies.

What did its AI reveal?

First, as suspected little has changed since the 1940s, men speak more and have more turns to speak then women do in films.

Year% Female Dialogue

(words spoken)% Male Dialogue (words spoken)

Best / Worst female dialogue representation

2019 31% 68%Highest: The Favourite (73%) and Roma (63%) Lowest: Bohemian Rhapsody (7%)

2018 31% 67%Highest: Lady Bird (80%) Lowest: Dunkirk (1%)

2017 29% 70%Highest: La La Land (50%) Hidden Figures (49%) Lowest: Hacksaw Ridge (8%)

However, digging deeper into the data there were noticeable changes.The range of female emotions being shown had increased markedly as historically female characters tended to stick to non-threatening feelings like joy and sadness, yet fear and disgust dominated this year. Female characters also used more forceful language. Among other things, that female relationships far outweighed male in the 2019 slate of movies. And that is a huge step forward from the traditional “Bechdel Test” based on whether two women talk to each other about something other than a man. The results show a promising trend toward better female characters and female-led stories, even without the screen time. To hear the whole story from StoryFit itself, click here.

Laying the data foundations for AIOur research confirms that as well as lagging behind other industries in AI, M&E is also off the pace in terms of data modernisation practices. Currently, the sectors with the highest percentage of companies categorised as AI leaders are the automotive, healthcare and banking industries – and it’s no coincidence that these industries are also very strong in data modernisation.

Take automotive. While most people associate AI in the auto industry with self-driving cars, automakers’ use of AI is actually very wide-ranging, across areas including driver-assist features, connected vehicles, manufacturing, quality control and product design. General Motors, for example, is using AI-driven “generative design” to shave unnecessary weight from car components, while Volkswagen is increasing the precision of its market forecasts with AI analytics, pulling in data like household income and customer preferences.

Targeting AI maturityBy their nature, industries like automotive had a head-start over M&E in the move to AI. These sectors typically started with more binary “cause-and-effect” data points to collect and manage from sensors detecting things like temperature, speed and engine performance, where a particular piece of data might trigger a specific action. That provided a basis for them to grow their AI maturity over time to tackle increasingly complex use cases. By contrast, the starting point for M&E data is already relatively complex and nuanced, ranging across emotional and psychological issues such as how to understand, influence and predict consumer behaviour.

Figure 3: Percentage of businesses expecting to reach advanced levels of AI maturity in three years’ time

This difference helps to explains why M&E companies have been relatively slow starters in AI. But they’re now looking to claw back the lost ground. As Figure 3 shows, the high proportion of M&E businesses who expect to be at a maturing or advanced stage of AI adoption in three years’ time points to a massive leap forward in AI capabilities.

This dramatic ramping-up of AI capabilities is a wise move. The move from B2B to B2C business models – powered by increasing direct-to-consumer delivery and consumption – is a seismic shift for media organisations. It means they have to deal with enormous amounts of data, certainly many magnitudes greater than ever before. They have an absolute need to analyse, understand and make decisions based on this data if they’re to survive, compete and transform for the digital era.

This often means removing data siloes. Many media organisations still have multiple separate systems each containing one part of the overall data picture. Usually these systems were built on a proprietary basis and are challenging to integrate with or extract data from – an issue that applies especially to systems relating to content and rights or to scheduling and media planning. Such a fragmented approach provides a poor starting point for higher-level AI activities

How to start?Many organisations we talk to get stuck on the problem of when to invest in data modernisation. The quandary is, should I modernise my entire data architecture first – then I’ll have the foundation to build AI models quickly and easily? Or should I just get started with a model and only modernise the data I really need to?

The problem with the first approach is that you may end up getting fired. You will have a wonderful data platform but will have delivered no value to the business. Conversely, if you take the latter approach and modernise just what you need to, you will spend much more overall and have to do a lot of re-work, as ideas you have later might be incompatible with the data architectures you initially designed.

We suggest a third option: invest in a strategy for your target data architecture, based on proven models and accelerators which will help you avoid common pitfalls or dead ends – but then align implementation of your target architecture to projects that create business value. This way you can limit re-work and get to results fast.

AI in Media & Entertainment: starting the journey to value

7 / AI: From Data to ROI < Back to Contents

A surge in AI maturity

Two-thirds of businesses in most industries will reach advanced levels of AI maturity Percent of respondents at a mature or advanced stage of AI.

Response base: 1,200 Source: ESI ThoughtLab/Cognizant Figure 3

Most companies, however, are in the early stages of AI adoption, with just 29% of respondents across industries at a maturing or advanced level in implementing AI (see Figure 3). Most AI projects are in pilot or early deployment stages, and even among AI leaders, just about one-quarter of AI projects are now in widespread deployment.

This will change dramatically, however, in the next three years, when the percent of businesses that expect to be at a maturing or advanced stage of AI adoption will more than double to 63%. In industries that are in the earlier stages of their AI journey – such as insurance, wealth and asset management, and media and entertainment – the increase will be fourfold.

Currently, the sectors with the highest percentage of AI leaders are the automotive, healthcare and banking industries. While the auto industry isn’t often the first sector that comes to mind when it comes to AI (beyond self-driving cars), automakers’ use of AI is far-ranging, including driver-assist features, connected vehicles, manufacturing, quality control and product design. General Motors, for example, is using AI-driven “generative design” to shave unnecessary weight from car parts, while Volkswagen is increasing the precision of its market forecasts with AI analytics, pulling in data on household income and customer preferences.

0 20 40 60 80 100

Automotive

Healthcare

Banks

Telecoms

Manufacturing

All industries

Life sciences

Technology

Energy and utilities

Consumer and retail

Media and entertainment

Insurance

Investment management 327%

306%

210%

136%

119%

117%

97%

84%

57%

52%

50%

273%

235%

% Change

Now

In three years

Modernising data = AI maturityOur research reveals a strong link between high AI maturity and effective data management, especially among companies that identify as AI leaders. So improving data management – or, as we at Cognizant term it, “modernising data” – needs to be a top area of focus for M&E companies to truly unlock the potential of AI.

The technological bottom line is that if a media company wants to invest in AI and deploy it effectively, data is the foundation. Take Universal music, which gets a billion data points per day from Spotify. Managing all that data – and keeping track of valuable insights into songs added and which user listens to what music and when – are impossible without AI.

As an organisation modernises its data and advances its AI capabilities, there is a natural evolution in terms of the buy-in. Once an M&E business is gathering and interpreting huge amounts of data, the business case for AI becomes much easier, as there is no other way to take advantage or make sense of it.

Case study:

Helping a leading media intelligence company apply AI to boost speed and efficiency while reducing cost

One of the world’s top media research, data and insights organisations needed to meet customers’ rising demand for real-time intelligence and advanced interactive features. To do this, it decided to revamp its applications landscape – which included 600+ applications across 36 countries – and build a global, scalable platform that would harness AI and automation to drive process efficiencies and faster turnaround.

In light of our deep domain expertise in data management and AI and strong track record as a single partner across IT and business process services, the company chose Cognizant to help build the solution. Applying our proven approach of “integrated transformation”, we began by engaging with the client across technology, innovation and operations to gain a holistic “T-shaped view” of its needs.

We then used this big-picture perspective to co-innovate to drive real-time data solutions while realising process efficiencies and cost savings. We also set up an AI Prototype Factory to identify use cases for quick turnaround. Today, the client is realising up to 25% cost savings and 40% productivity improvements while saving 40% to 60% of human effort through automated AI solutions. The result? It’s serving its customers better at lower cost. And leading innovation in its industry.

Investing in people and processes – as well as data and techAs is typical of organisations in the early stages of AI adoption, companies in the M&E industry are still facing challenges with IT architecture, data management and overall project management for AI projects. Data sources for AI applications are also expected to expand over the next three years from today’s images and text data to include moving video and high-dimensional data.

Most of the companies in our survey are intent on increasing their budget spend on AI over the next three years, with the majority of the funding going into technology rather than people or processes. Based on our global findings, this is once again indicative of “AI beginners” versus “AI leaders”: as companies’ AI maturity grows, the balance of their AI investments tend to shift towards people skills and business transformation.

It’s highly significant that organisations need to consider the brains, culture and transformational aspects as well as the technology to progress up the AI maturity curve. In fact, more value appears to be driven by the people and the culture than by the technology itself.

This is underlined by the fact that the companies emerging as AI leaders in our survey were the ones who knew how to try out many things rapidly and cheaply, but critically then also knew how to move from trial to production if appropriate. They did this by embedding technologists and data scientists within business teams. Doing this not only gives the technologists and scientists visibility of the business challenges and the ability to perceive the impact and value that is on the table – but also enables them to build the necessary relationships with the business teams who will apply, help to implement and then scale out the solutions.

A related consideration is that for technology to successfully transform your business, your people, leaders, structures, and values must all be aligned. This means organisations need to optimise their culture, leadership, and structures to enable successful use of AI and automation. The future of work involves human employees working side-by-side with robots, intelligent machines powered by AI, automation, and robotics. At Cognizant we’ve been working with forward-thinking media organisations for years, bringing our capabilities and resources to help meet their need for the right people and AI skills, technology and processes.

Value from AI grows and shifts externally as maturity risesOne of the most striking findings from our research is that as a company’s AI maturity grows, the outcomes and value it realises from AI both increase and change location. Since the M&E industry is currently in the early stages of AI adoption, the areas of value are mostly internally-focused such as higher productivity, customer retention and employee engagement. Leaders in AI, on the other hand, are able to use it to generate value externally, driving better strategic outcomes and growth.

What’s clear is that M&E companies cannot expect to launch their first AI project and immediately see the returns on investment start to flow in. AI takes some time to ramp up and is a long game for ROI. This means it needs sustained commitment and broad sponsorship from the executive leadership rather than just being treated as a project play. As in other sectors, disruptors in media are mainly data/tech companies – and competing with these is challenging unless an organisation is prepared to truly transform its business.

The need for long-term commitment is underlined by the fact that 65% of the M&E companies in our study are seeing returns of less than 5% from AI adoption, and none are seeing more than 5%. In contrast, nearly 40% of AI leaders across all industries report an average return of over 5%.

While it may seem logical to prioritise investment only in technology and transformation projects that offer a faster and more clearly understood return, the so called lower hanging fruit, consideration must be given to accelerating AI projects now due to the time lag to reach maturity and the competitive advantage this will enable in the future.

Conclusion: mapping out the road aheadA fundamental industry shift towards direct-to-consumer – powered by digitalisation and intensifying competition for audiences and content – is compelling M&E companies to mature their data and AI capabilities. The ability to manage, interpret and act on data on an individual level in real time is now pivotal to organisations’ future success. This means deploying AI. And while the industry is making progress with AI, most companies are still at the relatively early stages, building business cases and developing pilots – meaning they still have a long way to go to achieve maturity.

To accelerate and sustain their progress towards AI maturity, what companies should do now is ensure rock-solid senior sponsorship and invest in strategic data modernisation. Those elements provide the bedrock for testing out new use cases and running proofs-of-concept, before scaling up those ideas that fly and dropping those that don’t. At the same time, AI investments should start to rebalance away from tech towards vital people skills and process transformation using digital.

As you embark on this journey, don’t worry if the initial ROI from AI is hardly stellar: as our research confirms, AI maturity brings returns that are not only higher, but also extend into strategic execution and competitive advantage. AI is a long play, but a worthwhile one.

© Copyright 2020, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

World Headquarters500 Frank W. Burr Blvd.Teaneck, NJ 07666 USAPhone: +1 201 801 0233Fax: +1 201 801 0243Toll Free: +1 888 937 3277

European Headquarters1 Kingdom StreetPaddington CentralLondon W2 6BD EnglandPhone: +44 (0) 20 7297 7600Fax: +44 (0) 20 7121 0102

India Operations Headquarters#5/535 Old Mahabalipuram RoadOkkiyam Pettai, ThoraipakkamChennai, 600 096 IndiaPhone: +91 (0) 44 4209 6000Fax: +91 (0) 44 4209 6060